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1,730 results for “pollination”
Neutral processes related to regional bee commonness and dispersal distances are important predictors of plant-pollinator networks along gradients of climate and landscape conditions
<p>Understanding how niche-based and neutral processes contribute to the spatial variation in plant-pollinator interactions is central to designing effective pollination conservation schemes. Such schemes are needed to reverse declines of wild bees and other pollinating insects and to promote pollination services to wild and cultivated plants. We used data on wild bee interactions with plants belonging to the four tribes Loteae, Trifolieae, Anthemideae, and either spring- or summer-flowering Cichorieae, sampled systematically along a 682km latitudinal gradient to build models that allowed us to (a) predict occurrences of pairwise bee-flower interactions across 115 sampling locations, and (b) estimate the contribution of variables hypothesized to be related to niche-based assembly structuring processes (viz. annual mean temperature, landscape diversity, bee sociality, bee phenology, and flower preferences of bees) and neutral processes (viz. regional commonness and dispersal distance to conspecifics). While neutral processes were important predictors of plant-pollinator distributions, niche-based processes were reflected in the contrasting distributions of solitary bee and bumble bees along the temperature gradient, and in the influence of bee flower preferences on the distribution of bee species across plant types. In particular, bee flower preferences separated bees into three main groups, albeit with some overlap: visitors to spring-flowering Cichorieae; visitors to Anthemideae and summer-flowering Cichorieae; and visitors to Trifolieae and Loteae. Our findings suggest that both neutral and niche-based processes are significant contributors to the spatial distribution of plant-pollinator interactions so that conservation actions in our region should be directed towards areas: near high concentrations of known occurrences of regionally rare bees; in mild climatic conditions; and that are surrounded by heterogeneous landscapes. Given the observed niche-based differences, the proportion of functionally distinct plants in flower-mixes could be chosen to target bee species, or guilds, of conservation concern.</p>
Dataset of pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use
<p>This is the dataset of the manuscript entitled "Pollinator functional traits and interaction networks in neotropical mangroves: effects of patch size and surrounding land use", which was submitted for publication. The dataset include the functional traits of 162 insect pollinator species and 315 interactions with the mangrove species <em>Avicennia germinans, Conocarpus erectus, Laguncularia racemosa,</em> and <em>Rhizophora</em> <em>mangle</em>. The manuscript evaluates the effects of mangrove patch size and surrounding land use on pollinator functional diversity and plant-pollinator interactions in seven mangrove patches from the Colombian Caribbean region. Data variables are pollinator order, family, species, functional traits (pollinator guilds, body size, feeding preference, sociality, and nesting site) and frequency, interacting mangrove species, mangrove patch name, coordinates and size (ha), surrounding land use areas (urban areas, croplands, conserved dry forest, degraded vegetation areas, beach and water) and landscape diversity (Shannon H').</p>
Environmental stochasticity increases extinction risk to a greater degree in pollination specialists than in generalists
<p>Pollination sustains terrestrial food webs and agricultural systems and links the dynamics of interacting plant and pollinator species. Although environmental stochasticity is ubiquitous and can propagate through communities via species interactions in a way that increases extinction risk, it is unknown whether stochasticity affects species uniformly across pollination networks. In this paper, we introduce a stochastic dynamic model that makes novel use of the birth function and apply it to pollination networks of increasing size. We start with two- and four-species networks, in order to first illustrate the effects of stochasticity per se and then how those effects combine with specialization. We then describe the relationship between partner number and stochastic extinction risk in empirical networks with >20 species. In the 2-species network, increasing the variance of the stochastic term of the model increased the size of the region in parameter space where extinctions occur. In networks with 4 or more species, specialists were more vulnerable to extinction than generalists over a broad range of variances. Extinction risk in networks with >20 species declined nonlinearly with increasing mutualist partner number. Our results demonstrate the importance of including species interactions and stochasticity when using population-dynamic models to compare species' extinction risk. While models that omit either of these factors are likely to underestimate extinction risk, they disproportionately underestimate the vulnerability of specialists.</p>
Data from: Habitat quality influences pollinator pathogen prevalence through both habitat–disease and biodiversity–disease pathways
<p>The dilution effect hypothesis posits that increasing biodiversity reduces infectious disease transmission. Here, we propose that habitat quality might modulate this negative biodiversity–disease relationship. Habitat may influence pathogen prevalence directly by affecting host traits like nutrition and immune response (we coined this as the 'habitat–disease relationship') or indirectly by changing host biodiversity (biodiversity–disease relationship). We used a path model to test the relative strength of links between habitat, biodiversity, and pathogen prevalence in a pollinator–virus system. High-quality habitat metrics were directly associated with viral prevalence, providing evidence for a habitat–disease relationship. However, the strength and direction of specific habitat effects on viral prevalence varied based on the characteristics of the habitat, host, and pathogen. In general, more natural area and richness of landcover types were directly associated with increased viral prevalence, while greater floral density was associated with reduced viral prevalence. More natural habitat was also indirectly associated with reduced prevalence of two key viruses (black queen cell virus and deformed wing virus) via increased pollinator species richness, providing evidence for a habitat-mediated dilution effect on viral prevalence. Biodiversity–disease relationships varied across viruses, as prevalence of sacbrood virus was not associated with any habitat quality or pollinator community metrics. Across all viruses and hosts, habitat–disease and biodiversity–disease paths had effects of similar magnitude on viral prevalence. Therefore, habitat quality is a key driver of variation in pathogen prevalence among communities via both direct habitat–disease and indirect biodiversity–disease pathways, though the specific patterns varied among different viruses and host species. Critically, habitat–disease relationships could either contribute to or obscure dilution effects in natural systems depending on the relative strength and direction of the habitat–disease and biodiversity–disease pathways in that host–pathogen system. Therefore, habitat may be an important driver in the complex interactions between hosts and pathogens.</p>
Figures 2–7. Cayman Islands Sphingidae. 2 in A checklist of the hawkmoths (Lepidoptera: Sphingidae) of the Cayman Islands: with implications for the pollination of the ghost orchid Dendrophylax fawcettii Rolfe (Orchidaceae: Angraecinae) and consideration of bat predation
Figures 2–7. Cayman Islands Sphingidae. 2) Isognathus rimosa. 3) Erinnyis obscura. 4) Phryxus caicus. 5) Predation of Pachylia ficus larva by Mangrove Cuckoo, Coccyzus minor. 6) Pachylia ficus. 7) Enyo lugubris. Photographic credits: M.C. Rose-Smyth (2, 27.i.2017; 3, 08.viii.2018; 4, 24.xiii.2015, 6, 05.iv.2018; 7, 13.ii.2018), Yves-Jacques Rey-Millet (5, 29.xii.2012).
Figures 8–11. Cayman Islands Sphingidae. 8 in A checklist of the hawkmoths (Lepidoptera: Sphingidae) of the Cayman Islands: with implications for the pollination of the ghost orchid Dendrophylax fawcettii Rolfe (Orchidaceae: Angraecinae) and consideration of bat predation
Figures 8–11. Cayman Islands Sphingidae. 8) Eumorpha vitis. 9) Eumorpha fasciatus. 10) Eumorpha satellitia posticatus. 11) Xylophanes tersa. Photographic credits: Stuart Mailer (8, 12.v.2010), Peter and Norma Davey (9, 10.ii.2018), Gary J. Goss (10, 26.vi.2017), M.C. Rose-Smyth (11, NTCI collection).
Figure 1 in A checklist of the hawkmoths (Lepidoptera: Sphingidae) of the Cayman Islands: with implications for the pollination of the ghost orchid Dendrophylax fawcettii Rolfe (Orchidaceae: Angraecinae) and consideration of bat predation
Figure 1. Collection and observation locations in the Cayman Islands. Little Cayman: 1. Nature Trail; 2. Stonewall Dr., Spyglass Hill; 3. Pirates Point; 4. South Town (Blossom Village); 5. Cross the Land Road (now Guy Banks); 6. Central Forest, south of Sparrowhawk Hill; 7. Coppice Rd. Cayman Brac: A. West End (Cotton Tree Land); B. Stake Bay (Stakes Bay in Jordan 1940); C. Arlin Reid Drive; D. Earthquake Hole; E. Spot Bay; E1. Lighthouse Trail. Grand Cayman: F. West Bay; G. Crystal Harbour; H. George Town (Georgetown in Jordan 1940); I. Ocean Club; J. Newlands; K. North Sound Estates; L. Savannah; M. Agricultural Grounds/Pavilion/Lottery Rd.; N. Valley Gardens; O. Bodden Town; P. High Rock; Q. East End; R. Colliers Wilderness Reserve; S. Queen Elizabeth II Botanic Park; T. Old Man Bay; U. Mastic Trail; V. Hutland (Hut Rd.); W. North Side; X. North Sound, Booby Cay (Booby Bay in Jordan 1940).
Figure 12 in A checklist of the hawkmoths (Lepidoptera: Sphingidae) of the Cayman Islands: with implications for the pollination of the ghost orchid Dendrophylax fawcettii Rolfe (Orchidaceae: Angraecinae) and consideration of bat predation
Figure 12. Tongue lengths of twenty of the twenty-three species of hawkmoth found in Grand Cayman, plus that of Dolba hyloeus. Data from: Miller (1997) supplemented by Haber and Frankie (1989), Houlihan et al. (2019): and Danaher et al. (2019). Species are grouped by "pollinia carriers" and "visitors to flowers" in Florida, according to Houlihan et al. (2019) and Danaher et al. (2019) and "not observed". Colour codes are: red = species not occurring in Grand Cayman; blue = species occurring in Grand Cayman.
Fig. 2 in Pollination of Turnera subulata: exotic or native bees?
Fig. 2. Climatic factors (temperature, light intensity and relative humidity) along the floral longevity, covering the opening and senescence of the flowers of Turnera subulata Sm. in October, 2018, May and June, 2019 within the UEFS campus, Feira de Santana, BA, Brazil.
Differential impacts of land use change on multiple components of common Milkweed (Asclepias syriaca) pollination success
<p>Land-use change is one the greatest threats to biodiversity and is projected to increase in magnitude in the coming years, stressing the importance of better understanding how land-use change may affect vital ecosystem services, such as pollination. Past studies on the impact of land-use change have largely focused on only one aspect of the pollination process (e.g. pollinator composition, pollinator visitation, pollen transfer), potentially misrepresenting the full complexity of land-use effects on pollination services. Evaluating the impacts across multiple components of the pollination process can also help pinpoint the underlying mechanisms driving land-use change effects. This study evaluates how land-use change affects multiple aspects of the pollination process in common milkweed populations, including pollinator community composition, pollinator visitation rate, pollen removal, and pollen deposition. Overall, land-use change altered floral visitor composition, with small bees having a larger presence in developed areas. Insect visitation rate and pollen removal were also higher in more developed areas, perhaps suggesting a positive impact of land-use change. However, pollen deposition did not differ between developed and undeveloped sites. Our findings highlight the complexity evaluating land-use change effects on pollination, as these likely depend on the specific aspect of pollination evaluated and on the of the intensity of disturbance. Our study stresses the importance of evaluating multiple components of the pollination process in order to fully understand overall effects and mechanisms underlying land-use change effects on this vital ecosystem service.</p>
Species-specific differences in bumblebee worker body size between elevations: Implications for pollinator community structure under climate change
<p>Code and dataset for manuscript titled "<span>Species-specific differences in bumblebee worker body size between elevations: Implications for pollinator community structure under climate change". Authors: Caterina Massa, Janneke Hille Ris Lambers, Sarah K. Richman. Manuscript accepted to Journal of Pollination Ecology in May 2024. All data collected and analyzed by the authors.<br></span></p>
Data from Investigating the effects of diurnal and nocturnal pollinators on male and female reproductive success and on floral trait selection in Silene dioica
<p><strong>data_all_OdEx.csv</strong>: all data about phenotypes or reproductive success at the individual scale</p> <ul> <li>ID : ID name</li> <li>nGrSemis_min : seed number needed to be sowned to get enough seedlings</li> <li>nGrGerm : seed number effectively sowned</li> <li>nGrGerm_OK : number of germinated seed</li> <li>TauxGerm : germination rate</li> <li>nFruits_MAX : maximal number of fruit that the plant could have produced</li> <li>nFruits_OK : effective number of fruits that the plant had produced</li> <li>nFruits_OK_avecPred : effective number of fruits that the plant had produced ignoring predation</li> <li>nFruits_pred : number of predated fruits</li> <li>mean_nbSeeds : mean number of seeds per fruit</li> <li>sd_nbSeeds : sd number of seeds per fruit</li> <li>mean_nbOv : mean ovule non fertilize per fruit</li> <li>sd_nbOv : sd ovule non fertilize per fruit</li> <li>mean_nbOvTOT : mean ovule number per flower</li> <li>sd_nbOvTOT : sd ovule number per flower</li> <li>prodTOT : total number of seed produced including germination rate</li> <li>FS : Fruit-set</li> <li>SS : Seed-set</li> <li>prodTOTsg : total number of seed produced without germination rate</li> <li>nbFlo_run0 : flower number at the beginning of the experiment</li> <li>nbFlo_run1 : flower number at the first measurement</li> <li>mean_nbFlo : mean flower number</li> <li>MeanFec : mean seed sired per males according to MEMM model</li> <li>MeanDelta : mean delta pollen dispersion according to MEMM model</li> <li>MeanMRS : mean male reproductive success (including female RS) according to MEMM model</li> <li>MedFec : same as above with the median</li> <li>MedDelta : same as above with the median</li> <li>MedMRS : same as above with the median</li> <li>VarFec : same as above with the variance</li> <li>VarDelta : same as above with the variance</li> <li>VarMRS : same as above with the variance</li> <li>ciFec : Same as above with confidence interval</li> <li>ciDelta : Same as above with confidence interval</li> <li>ciMRS : Same as above with confidence interval</li> <li>MS_Res : mating success</li> <li>mean_lFl : mean corolla width</li> <li>mean_hFl : mean calyx height</li> <li>QttTOT : pollen number per flower</li> <li>pop : which originate population</li> <li>cohort : which cohort</li> </ul> <p><strong>data_seeds_OdEx.csv</strong> : all data about seed number of weight as well as unfertilized ovule at the fruit scale for female RS</p> <ul> <li>ID : ID name</li> <li>noFruit : ID fruit</li> <li>poids : seed weight</li> <li>nbSeeds : number of seeds</li> <li>nbOv : number of unfertilized ovule</li> <li>moySeeds : mean seed size</li> <li>varSeeds : variance in seed size</li> </ul> <p><strong>data_poll_OdEx.csv</strong> : all data about pollinator observation session</p> <ul> <li>ID : ID name</li> <li>session : observation session number</li> <li>nbVis : number of independent insect attracted</li> <li>nbVisTot : number of total visit</li> <li>binVis : individual visited or not</li> </ul>
Data from: Climatic conditions and landscape diversity predict plant-bee interactions and pollen deposition in bee-pollinated plants.
<p>Climate change, landscape homogenization and the decline of beneficial insects threaten pollination services to wild plants and crops. Understanding how pollination potential (i.e. the capacity of ecosystems to support pollination of plants) is affected by climate change and landscape homogenization is fundamental for our ability to predict how such anthropogenic stressors affect plant biodiversity. Models of pollinator potential are improved when based on pairwise plant-pollinator interactions and pollinator´s plant preferences. However, whether the sum of predicted pairwise interactions with a plant within a habitat (a proxy for pollination potential) relates to pollen deposition on flowering plants has not yet been investigated. We sampled plant-bee interactions in 68 Scandinavian plant communities in landscapes of varying land-cover heterogeneity along a latitudinal temperature gradient of 4–8 C°, and estimated pollen deposition as the number of pollen grains on flowers of the bee-pollinated plants <em>Lotus corniculatus</em>, and <em>Vicia cracca</em>. We show that plant-bee interactions, and the pollination potential for these bee-pollinated plants increase with landscape diversity, annual mean temperature, plant abundance, and decrease with distances to sand-dominated soils. Furthermore, the pollen deposition in flowers increased with the predicted pollination potential, which was driven by landscape diversity and plant abundance. Our study illustrates that the pollination potential, and thus pollen deposition, for wild plants can be mapped based on spatial models of plant-bee interactions that incorporate pollinator-specific plant preferences. Maps of pollination potential can be used to guide conservation and restoration planning.</p>
Figure 5 in Contribution of Insect Pollination to Macadamia integrifolia Production in Hawaii
Figure 5. Mean (± SE) macadamia nut yield for open-pollination and insect exclusion treatments for year I and II combined. Bars with different letters were significantly different (P <0.0001) Students t-test.
Figure 2 in Contribution of Insect Pollination to Macadamia integrifolia Production in Hawaii
Figure 2. (A) Immature macadamia inflorescence, (B) macadamia inflorescence one day before opening with a syrphid, Ornidia obesa, and (C) macadamia inflorescence that has flowers completely open with a honeybee, Apis mellifera.
Figure 4 in Contribution of Insect Pollination to Macadamia integrifolia Production in Hawaii
Figure 4. (A) Initial fruit set per raceme at 14 days after flowering, and (B) fruit retention at 2 months (2010, n = 10) and 3 months (2011, n = 40) after flowering (means ± SE). Bars with different letters (within a year) were significantly different (P <0.001), Students t-tests.
Figure 1 in Contribution of Insect Pollination to Macadamia integrifolia Production in Hawaii
Figure 1. Map of orchard at the University of Hawaii Waimanalo Research Station showing transect route.
Figure 3 in Contribution of Insect Pollination to Macadamia integrifolia Production in Hawaii
Figure 3. Progression of a macadamia flower: (A) macadamia flower one day before opening with its pistil beginning to emerge from perianth and creating a loop, (B) macadamia flower that has just opened, note pollen on the stigma, (C) two days old, note stigma is free of pollen, (D) three days old, and (E) four days old, petaloid sepals have turned brown and will soon drop off. Stigma is receptive around 2–3 days.
Fig. 1 in Wood Properties Of Nine Pinus Sylvestris Open-Pollinated Families Originating From Different Lithuanian Populations
Fig. 1. Field trials with open-pollinated progeny of Lithuanian Pinus sylvestris L. populations, established in 1983.
Fig 4 in Wood Properties Of Nine Pinus Sylvestris Open-Pollinated Families Originating From Different Lithuanian Populations
Fig 4. Wood ring width (total, early- and latewood) of nine Scots pine half-sib families in different field trials and forest types. Error bars indicate standard deviation.
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